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How can AI in credit scoring enhance risk assessment?

 

    In many financial institutions, the credit scoring process still relies on the traditional scorecard approach developed at its inception. To be considered “scorable,” a prospective borrower must have a sufficient history of past borrowing behavior. New customers in the banking sector often face challenges in getting credit because they lack historical data, even if they are creditworthy.
    In contrast, AI-powered credit scoring takes a more dynamic and real-time approach to assessing a potential borrower’s creditworthiness. It considers their current income level, employment prospects, and potential earning capacity. This means that borrowers with high potential are more likely to be included in credit programs, while those who might pass traditional credit scoring assessments but exhibit risky behaviors (e.g., frequent credit card churning) can be excluded.
    AI-based credit scoring enables more precise predictions by leveraging smart AI models considering a broader range of real-time indicators, allowing financial institutions to make more informed lending decisions.

 

Types of credit scoring models
     Credit scoring models are primarily categorized into statistical and judgmental scoring models, each with its approach to assessing an individual’s creditworthiness.

 

Statistical scoring models: Statistical scoring models use a data-driven approach by analyzing various factors gathered from credit reporting agencies. These factors may include payment history, credit utilization, length of credit history, types of credit accounts, and recent credit inquiries. The model then correlates and analyzes these factors, assigning specific weights based on their impact on creditworthiness. The scoring process is purely objective and is not influenced by personal judgments or experiences of credit officials. The resulting credit score is a numerical representation of the individual’s credit risk based on a statistical analysis of their financial behavior.

 

Judgmental scoring models: Judgmental scoring models take a more subjective approach, considering objective financial data and subjective assessments. These models include an individual or organizational financial statement, payment history, bank references, and subjective judgment of human underwriters in decision-making. This scoring model allows for a more personalized evaluation, considering financial data and the context and circumstances surrounding an applicant’s credit history.
Statistical scoring models rely on a statistical analysis of quantifiable data, while judgmental scoring models incorporate personal assessments and experiences in determining credit scores.      Both models have their own merits and are utilized based on the specific needs and preferences of the lending institution or organization.
The foundation of most credit scoring models relied heavily on past payment history and was developed using statistical analysis methods like linear regression, decision trees, logit modeling, and others. These traditional models used limited structured data to assess credit risk.

 

Linear regression: In regression-based credit scoring models, the objective is to predict and explain credit risk and the likelihood of default. This is achieved by analyzing structured data, where the focus is the target outcome (e.g., default/non-default). The structured data includes various independent variables or factors related to an individual’s credit history, financial standing, and other relevant information.
    The process involves finding the best-fitting parameters that minimize the differences between the predicted credit risk (based on these independent variables) and the actual observed credit risk. This is achieved through regression analysis, a statistical technique that identifies the relationships between the target outcome and the independent variables.
The regression model aims to create a mathematical equation that represents this relationship. For instance, as an individual’s debt increases, their probability of default also increases. The parameters are optimized to create the most accurate prediction of credit risk based on the provided data.
     This method allows lenders and financial institutions to gain valuable insights into the factors influencing credit risk. This understanding ensures that they make informed decisions about extending credit to an applicant and under what terms, aiding in assessing potential risks associated with the loan.

 

Discriminant analysis: Discriminant models are objective methods of finding the differences between good and bad customers. By applying discriminant analysis, lending firms can discriminate good credit customers from bad ones. Lenders often look for a method to identify bad customers by using data from the customer’s financial statements. In that way, using simple discriminant analysis goes a long way in providing a dependable solution to the lenders.
Each customer is assigned a composite score. Lenders can then set a minimum score to distinguish good customers from bad ones. Unlike the simpler model that only considers two factors, the advanced discriminant analysis approach considers numerous factors that can influence credit scores. These factors interact, and the model assigns appropriate weight to each factor to create a more comprehensive credit scoring system.

 

 
Deep neural networks: These models learn to discern data patterns through iterative processing across multiple layers rather than relying on predefined equations. They adapt and enhance their understanding by incorporating outputs from preceding layers, enabling the detection of complex, nonlinear patterns in unstructured data.

 

Clustering: The credit scoring approach categorizes data into distinct clusters exhibiting notable differences. In this context, a clustering algorithm might create a specific cluster for borrowers whose creditworthiness is challenging to evaluate accurately. Once this cluster is identified, the average default rate or assessment within that cluster can serve as a reference point for estimating the probability of default for individual borrowers within the same cluster. It leverages similarities within clusters to provide more precise credit risk assessments for borrowers with similar characteristics, making it a valuable tool for lenders in assessing creditworthiness.
 
Types of credit scoring models used in Finance
 
  • FICO score
    The most common example of a credit scoring model is FICO. A FICO scoring model produces credit scores by giving consumers a rating between 300 and 850, with a score above 740 considered good.
     Key factors considered by the FICO model are payment history, credit utilization, credit history and usage, new credit, etc. Alternative credit scoring models emphasize different factors.
In the case of FICO 10 (debuted in 2021), for example, there is no penalty on unpaid medical bills and rental history is considered a factor different from the previous version of this model and alternative credit scoring models.
  • VantageScore
     Vantage Score Model, which debuted in 2006 to compete with FICO. It also looks at several crucial factors like credit card balance, status of new credit obligations, number of bank accounts, etc. This model emphasizes payment history, age, and type of credit, while the customer’s recent behavior is given less weightage.
     Apart from these two, several less prominent credit models exist, such as CreditXpert, Experian’s National Equivalency, and TransRisk.
  • CreditXpert
    CreditXpert aims to aid businesses in evaluating prospective account holders. By scrutinizing credit histories, it seeks methods to boost scores or identify inaccuracies swiftly. Enhancing these scores can result in increased approval rates for customer loans.
  • Experian’s National Equivalency Score
    This model assigns users with a credit score ranging from 0 to 1,000. It considers factors such as credit length, mix, utilization, payment history, total balances, etc. However, Experian has not disclosed the specific criteria or their weights regarding calculating the score.
  • TransRisk score
    This model relies on information sourced from TransUnion. It is designed to evaluate a customer’s risk specifically for new accounts rather than existing ones. The TransRisk score, however, is underutilized by lenders due to its specialized focus (as limited information about the score is accessible). It has been found that in most cases, individuals’ TransRisk scores tend to be considerably lower than their FICO scores.
 
Credit Risk Scoring: Approach guidelines
     The credit scores can hugely impact an individual’s financial and personal life. Therefore, several guidelines have been set for building a credit score model. For example, such models should be transparent and unbiased. The data used for model building should be of high quality. However, developers must consider privacy and regulatory compliance to obtain high-quality data. Overall, a credit scoring model must be unbiased and robust to anomalies.
     The World Bank has written a detailed document that outlines the guidelines for credit scoring approaches, explains the importance of effective credit risk assessment in financial institutions, and discusses the various methods for evaluating creditworthiness, including traditional scoring models and newer, more sophisticated techniques.
     As you can see, several ways exist of creating a credit scoring model. Let’s examine the most crucial types.
Credit scoring models can be divided into several categories.
  • Individual Scoring
     Firstly, individual customer scoring models assess individuals’ creditworthiness based on several demographic factors (age, education, experience, etc.) and financial characteristics (recurring expenses, outstanding debt, etc.). Such a model can further be divided into the type of product, such as credit card or mortgage, with the latter being more comprehensive than the former.
  • Enterprise Scoring
    Another category of credit risk models is Enterprise Scoring, where companies are assessed. Such business credit scoring models evaluate a company’s structure, competition, employee status, source of financing, etc. For smaller companies, the owner’s profile is also examined.
  • Internal and External Scoring
     Credit models can also be differentiated based on the type of developers. They can be internal (developed by banks for their customers and loan applicants) or external (provided by specialized institutions like credit bureaus).
    The World Bank guidelines point towards using different credit scoring model methodologies, with the primary categories being traditional and modern. In the next section, we will discuss both these types of models.
 
How do credit risk models add value to business?
     In the financial business world, credit scoring models play a crucial role. It adds value to business in the following manner-
  • Credit Risk Management
   Credit Risk Models allow lenders to evaluate the creditworthiness of individuals and organizations and ensure that their exposure to liability is manageable. This allows lenders to assess the level of risk in their loan portfolio.
  • Regulatory compliance
     Today, credit risk models are required by law. Basel III (a set of international banking regulations) requires banks to use such models to meet regulatory requirements, where they are expected to maintain a certain amount of capital based on the credit risk exposure indicated by the credit risk model.
  • Scenario Analysis
Stress testing can be done through credit score models, which allow lending firms to analyze various scenarios and test the resilience of their loan portfolio. This, in turn, helps manage losses in case of events like recessions and other financial crises.
Credit risk models also allow lending firms to stay competitive, reduce costs, and mitigate risk. As you can see, several credit score models are used. However, some serious downsides must be discussed along with their various upsides.

 

Limitations of Credit Scoring Models
     Until recently, most credit risk scoring models were based on analyzing past payment patterns using linear and logistic regression statistical algorithms. This method allowed different weights to be found for different factors. Models created using such methods are called traditional credit scoring models.

      With the advent of smartphones, the availability of the internet, and several other factors, new kinds of data are now available. The data can now be semi-structured, unstructured, and huge, but it can provide a much more granular understanding of the customer and their creditworthiness. This is where new modern credit scoring models that use machine learning algorithms and deep neural networks have come into play. Such models can detect patterns and can provide a much more reliable score. The issue, however, with them is that they are much less interpretable and, therefore, can be biased.

      One must know the limitations when developing and deploying a credit risk model. Some of the key ones are as follows-
  • Devoid of Context
Credit scoring models don’t factor in the current economic conditions, as a borrower with a high score may default, given that the economic conditions change for the worse. They also don’t consider individuals’ life events, market trends, and other macroeconomic factors.
  • Dependent on Data
Such models are highly dependent on the quality of data and its availability, as gaps and anomalies in the data can greatly adversely impact the model’s accuracy. There is an overreliance on historical data when building such models, and a customer’s recent change in behavior is often overlooked.
  • Biased
Often, such models are biased toward certain demographic groups and credit applicants. This is often systemic discrimination in the historical data that passes to the models.
  • Susceptible to Fraud
If one knows the loopholes in calculating the credit score, individuals can manipulate their creditworthiness, making such models susceptible to fraud.
  • Opaque
Advanced models are often highly complex, making understanding how a score is calculated difficult. This opaqueness leads to a lack of public trust in such models and a general sense of animosity towards the financial system.
Now that all of the major aspects of credit score models have been covered, it’s time to create one of your own.
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